Elucidating Spatially-Resolved Changes in Host Signaling During Plasmodium Liver-Stage Infection

Upon transmission to the human host, Plasmodium sporozoites exit the skin, are taken up by the blood stream, and then travel to the liver where they infect and significantly modify a single hepatocyte. Low infection rates within the liver have made proteomic studies of infected hepatocytes challenging, particularly in vivo, and existing studies have been largely unable to consider how protein and phosphoprotein differences are altered at different spatial locations within the heterogeneous liver. Using digital spatial profiling, we characterized changes in host signaling during Plasmodium yoelii infection in vivo without disrupting the liver tissue. Moreover, we measured alterations in protein expression around infected hepatocytes and identified a subset of CD163+ Kupffer cells that migrate towards infected cells during infection. These data offer the first insight into the heterogeneous microenvironment that surrounds the infected hepatocyte and provide insights into how the parasite may alter its milieu to influence its survival and modulate immunity.

Graphics processing unit-accelerated Monte Carlo simulation of polarized light in complex three-dimensional media

Significance: Monte Carlo (MC) methods have been applied for studying interactions between polarized light and biological tissues, but most existing MC codes supporting polarization modeling can only simulate homogeneous or multi-layered domains, resulting in approximations when handling realistic tissue structures. Aim: Over the past decade, the speed of MC simulations has seen dramatic improvement with massively-parallel computing techniques. Developing hardware-accelerated MC simulation algorithms that can accurately model polarized light inside 3-D heterogeneous tissues can greatly expand the utility of polarization in biophotonics applications. Approach: Here we report a highly efficient polarized MC algorithm capable of modeling arbitrarily complex media defined over a voxelated domain. Each voxel of the domain can be associated with spherical scatters of various radii and densities. The Stokes vector of each simulated photon packet is updated through photon propagation, creating spatially resolved polarization measurements over the detectors or domain surface. Results: We have implemented this algorithm in our widely disseminated MC simulator, Monte Carlo eXtreme (MCX). It is validated by comparing with a reference CPU-based simulator in both homogeneous and layered domains, showing excellent agreement and a 931-fold speedup. Conclusion: The polarization-enabled MCX (pMCX) offers biophotonics community an efficient tool to explore polarized light in bio-tissues, and is freely available at http://mcx.space/.

Spatially Resolved Operando Synchrotron-Based X-Ray Diffraction Measurements of Ni-Rich Cathodes for Li-Ion Batteries

Understanding the performance of commercially relevant cathode materials for lithium-ion (Li-ion) batteries is vital to realize the potential of high-capacity materials for automotive applications. Of particular interest is the spatial variation of crystallographic behavior across (what can be) highly inhomogeneous electrodes. In this work, a high-resolution X-ray diffraction technique was used to obtain operando transmission measurements of Li-ion pouch cells to measure the spatial variances in the cell during electrochemical cycling. Through spatially resolved investigations of the crystallographic structures, the distribution of states of charge has been elucidated. A larger portion of the charging is accounted for by the central parts, with the edges and corners delithiating to a lesser extent for a given average electrode voltage. The cells were cycled to different upper cutoff voltages (4.2 and 4.3 V vs. graphite) and C-rates (0.5, 1, and 3C) to study the effect on the structure of the NMC811 cathode. By combining this rapid data collection method with a detailed Rietveld refinement of degraded NMC811, the spatial dependence of the degradation caused by long-term cycling (900 cycles) has also been shown. The variance shown in the pristine measurements is exaggerated in the aged cells with the edges and corners offering an even lower percentage of the charge. Measurements collected at the very edge of the cell have also highlighted the importance of electrode alignment, with a misalignment of less than 0.5 mm leading to significantly reduced electrochemical activity in that area.

Bulk and spatially resolved extracellular metabolomics of free-living nitrogen fixation

Soil microorganisms drive ecosystem function, but challenges of scale between microbe and ecosystem hinder our ability to accurately quantify and predictively model the soil microbe-ecosystem function relationship. Quantifying this relationship necessitates studies that systematically characterize multi-omics of soil microorganisms and their activity across sampling scales from spatially resolved to bulk measures, and structural complexity, from liquid pure culture to in situ. To address this need, we cultured two diazotrophic bacteria in liquid and solid media, with and without nitrogen (N) to quantify differences in extracellular metabolites associated with nitrogen fixation under increasing environmental structural complexity. We also quantified extracellular metabolites across sampling scales including bulk sampling via GC-MS analysis and spatially resolved analysis via MALDI mass spectrometry imaging. We found extracellular production of inorganic and organic N during free-living nitrogen fixation activity, highlighting a key mechanism of terrestrial N contributions from this process. Additionally, our results emphasize the need to consider the structural complexity of the environment and spatial scale when quantifying microbial activity. We found differences in metabolite profiles between culture conditions, supporting previous work indicating environmental structure influences microbial function, and across scales, underscoring the need to quantify microbial scale conditions to accurately interpret microbial function.

Identifying temporal and spatial patterns of variation from multimodal data using MEFISTO

Factor analysis is a widely used method for dimensionality reduction in genome biology, with applications from personalized health to single-cell biology. Existing factor analysis models assume independence of the observed samples, an assumption that fails in spatio-temporal profiling studies. Here we present MEFISTO, a flexible and versatile toolbox for modeling high-dimensional data when spatial or temporal dependencies between the samples are known. MEFISTO maintains the established benefits of factor analysis for multimodal data, but enables the performance of spatio-temporally informed dimensionality reduction, interpolation, and separation of smooth from non-smooth patterns of variation. Moreover, MEFISTO can integrate multiple related datasets by simultaneously identifying and aligning the underlying patterns of variation in a data-driven manner. To illustrate MEFISTO, we apply the model to different datasets with spatial or temporal resolution, including an evolutionary atlas of organ development, a longitudinal microbiome study, a single-cell multi-omics atlas of mouse gastrulation and spatially resolved transcriptomics.

Deconstructing Pancreatic Cancer Using Next Generation-Omic Technologies–From Discovery to Knowledge-Guided Platforms for Better Patient Management

Comprehensive molecular landscaping studies reveal a potentially brighter future for pancreatic ductal adenocarcinoma (PDAC) patients. Blood-borne biomarkers obtained from minimally invasive “liquid biopsies” are now being trialled for early disease detection and to track responses to therapy. Integrated genomic and transcriptomic studies using resectable tumour material have defined intrinsic patient subtypes and actionable genomic segments that promise a shift towards genome-guided patient management. Multimodal mapping of PDAC using spatially resolved single cell transcriptomics and imaging techniques has identified new potentially therapeutically actionable cellular targets and is providing new insights into PDAC tumour heterogeneity. Despite these rapid advances, defining biomarkers for patient selection remain limited. This review examines the current PDAC cancer biomarker ecosystem (identified in tumour and blood) and explores how advances in single cell sequencing and spatially resolved imaging modalities are being used to uncover new targets for therapeutic intervention and are transforming our understanding of this difficult to treat disease.

Developmental origins of cell heterogeneity in the human lung

The lung contains numerous specialized cell-types with distinct roles in tissue function and integrity. To clarify the origins and mechanisms generating cell heterogeneity, we created a first comprehensive topographic atlas of early human lung development. We report 83 cell states, several spatially-resolved developmental trajectories and predict cell interactions within defined tissue niches. We integrated scRNA-Seq and spatial transcriptomics into a web-based, open platform for interactive exploration. To illustrate the utility of our approach we show distinct states of secretory and neuroendocrine cells, largely overlapping with the programs activated either during lung fibrosis or small cell lung cancer progression. We define the origin of uncharacterized airway fibroblasts associated with airway smooth muscle in bronchovascular bundles, and describe a trajectory of Schwann cell progenitors to intrinsic parasympathetic neurons controlling bronchoconstriction. Our atlas provides a rich resource for further research and a reference for defining deviations from homeostatic and repair mechanisms leading to pulmonary diseases.

Single Cell Spatial Analysis Reveals the Topology of Immunomodulatory Purinergic Signaling in Glioblastoma

Glioblastoma develops an immunosuppressive microenvironment that fosters tumorigenesis and resistance to current therapeutic strategies. Here we use multiplexed tissue imaging and single-cell RNA-sequencing to characterize the composition, spatial organization, and clinical significance of extracellular purinergic signaling in glioblastoma. We show that glioblastoma exhibit strong expression of CD39 and CD73 ectoenzymes, correlating with increased adenosine levels. Microglia are the predominant source of CD39, while CD73 is principally expressed by tumor cells, particularly in tumors with amplification of EGFR and astrocyte-like differentiation. Spatially-resolved single-cell analyses demonstrate strong spatial correlation between tumor CD73 and microglial CD39, and that their spatial proximity is associated with poor clinical outcomes. Together, this data reveals that tumor CD73 expression correlates with tumor genotype, lineage differentiation, and functional states, and that core purine regulatory enzymes expressed by neoplastic and tumor-associated myeloid cells interact to promote a distinctive adenosine-rich signaling niche and immunosuppressive microenvironment potentially amenable to therapeutic targeting.

Eddy Covariance Measurements Highlight Sources of Nitrogen Oxide Emissions Missing from Inventories for Central London

During March–June 2017 emissions of nitrogen oxides were measured via eddy covariance at the British Telecom Tower in central London, UK. Through the use of a footprint model the expected emissions were simulated from the spatially resolved National Atmospheric Emissions Inventory for 2017, and compared with the measured emissions. These simulated emissions were shown to underestimate measured emissions during the day time by a factor of 1.48, but they agreed well overnight. Furthermore, underestimations were spatially mapped and the areas around the measurement site responsible for differences in measured and simulated emissions inferred. It was observed that areas of higher traffic, such as major roads near national rail stations, showed the greatest underestimation by the simulated emissions. These discrepancies are partially attributed to a combination of the inventory not fully capturing traffic conditions in central London, and both spatial and temporal resolution of the inventory not fully describing the high heterogeneity of the urban centre. Understanding of this underestimation may further improved with longer measurement time series ,to better understand temporal variation, and improved temporal scaling factors, to better simulate sub-annual emissions.